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A power failure sensitivity early warning method and device based on heterologous cross regression analysis

A regression analysis and early warning device technology, applied in the electric power field, can solve problems such as interference of early warning indicators, failure to consider impact differences, customer power outage sensitivity analysis cannot well reflect the weight of different influencing factors, etc., to reduce the probability of complaints, The effect of improving customer satisfaction

Pending Publication Date: 2019-06-25
STATE GRID HENAN ELECTRIC POWER ELECTRIC POWER SCI RES INST +2
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In the existing logistic regression process, the logistic regression method usually combines all possible risk-influencing factor variables into a joint model input, without considering the impact difference among them. In this way, the obtained customer outage sensitivity The analysis also cannot reflect the weight of different influencing factors well
It will interfere with the selection of early warning indicators

Method used

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  • A power failure sensitivity early warning method and device based on heterologous cross regression analysis
  • A power failure sensitivity early warning method and device based on heterologous cross regression analysis
  • A power failure sensitivity early warning method and device based on heterologous cross regression analysis

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Embodiment 1

[0040] A method for early warning of power outage sensitivity based on heterogeneous cross regression analysis, comprising the following steps:

[0041] Form a sample data set: use customers who have consulted about power outages as power outage sensitive customers to form a sensitive user sample set, and customers who have not consulted about power outages to form a non-sensitive user sample set for non-power outage sensitive customers, from the sensitive user sample set and non-power outage sensitive customers The sensitive user sample set is randomly selected in a specific proportion to form a sample data set;

[0042] Selected variable factors: Obtain the basic customer information corresponding to the sample data set, electricity consumption information, payment information, and power outage event formation, including at least six variables: measurement method, contract capacity, average electricity price, industry type, power supply unit, and historical 95598 call times ...

Embodiment 2

[0086] This embodiment also provides a power outage sensitivity early warning device based on heterogeneous cross regression analysis, including:

[0087] Form sample data set module: It is used to form a sample set of sensitive users for customers who are sensitive to power outages from customers who have consulted about power outages, and a non-sensitive user sample set for non-power outage sensitive customers from customers who have not consulted about power outages. From sensitive user samples The sample data set is formed by random selection in a specific proportion from the sample set and the non-sensitive user sample set;

[0088] Selected variable factor module: used to obtain basic customer information, electricity consumption information, payment information, and power outage event formation corresponding to the sample data set, including at least measurement method, contract capacity, average electricity price, industry type, power supply unit, and historical 95598 c...

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Abstract

The invention discloses a power failure sensitivity early warning method and device based on heterologous cross regression analysis. A sample data set is formed, related performance measures of the variable factors are evaluated by selecting the variable factors, a certain threshold value is set for the variable factors according to the related performance measures of the customer power failure satisfaction degree of the variable factors, and early warning is conducted on customer power failure complaints. The power failure sensitivity early warning method and device based on heterologous cross regression analysis are helpful for power enterprises to accurately identify customers with high power failure sensitivity, reduce the probability of complaints caused by electricity charge errors of the customers, and achieve the purpose of integrally improving the satisfaction degree of the customers.

Description

technical field [0001] The invention relates to the field of electric power, in particular to a power failure sensitivity early warning model based on heterogeneous cross regression analysis. Background technique [0002] Power grid companies have large customers and complex production and operation conditions. At present, frequent power outage complaints have become the main source of power customer complaints, seriously affecting the improvement of customer service satisfaction. In order to improve service quality and improve operation and maintenance efficiency, based on big data analysis and mining technology, from a large amount of business data, through learning, regression, and classification algorithms, discover hidden relationships between business data, and find customers who have complaints, consultations, etc. Influencing factors, preventive measures and service preparations should be done in advance to improve work quality and service levels. [0003] In curre...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/06
Inventor 李孟超李晓蕾耿俊成张小斐王自强袁少光万迪明刘玮田杨阳
Owner STATE GRID HENAN ELECTRIC POWER ELECTRIC POWER SCI RES INST
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